A review of deep learning techniques for speech processing
The field of speech processing has undergone a transformative shift with the advent of deep
learning. The use of multiple processing layers has enabled the creation of models capable …
learning. The use of multiple processing layers has enabled the creation of models capable …
A metaverse: Taxonomy, components, applications, and open challenges
SM Park, YG Kim - IEEE access, 2022 - ieeexplore.ieee.org
Unlike previous studies on the Metaverse based on Second Life, the current Metaverse is
based on the social value of Generation Z that online and offline selves are not different …
based on the social value of Generation Z that online and offline selves are not different …
End-to-end spectro-temporal graph attention networks for speaker verification anti-spoofing and speech deepfake detection
Artefacts that serve to distinguish bona fide speech from spoofed or deepfake speech are
known to reside in specific subbands and temporal segments. Various approaches can be …
known to reside in specific subbands and temporal segments. Various approaches can be …
Implicit neural spatial filtering for multichannel source separation in the waveform domain
We present a single-stage casual waveform-to-waveform multichannel model that can
separate moving sound sources based on their broad spatial locations in a dynamic …
separate moving sound sources based on their broad spatial locations in a dynamic …
Complex-valued spatial autoencoders for multichannel speech enhancement
MM Halimeh, W Kellermann - ICASSP 2022-2022 IEEE …, 2022 - ieeexplore.ieee.org
In this contribution, we present a novel online approach to multichannel speech
enhancement. The proposed method estimates the enhanced signal through a filter-and …
enhancement. The proposed method estimates the enhanced signal through a filter-and …
A novel approach to multi-channel speech enhancement based on graph neural networks
Multi-channel speech enhancement aims at utilizing spatial relationships between signals
captured from a microphone array along with temporal-spectral information efficiently to …
captured from a microphone array along with temporal-spectral information efficiently to …
Leveraging low-distortion target estimates for improved speech enhancement
A promising approach for multi-microphone speech separation involves two deep neural
networks (DNN), where the predicted target speech from the first DNN is used to compute …
networks (DNN), where the predicted target speech from the first DNN is used to compute …
Time-domain speech separation networks with graph encoding auxiliary
End-to-end time-domain speech separation with masking strategy has shown its
performance advantage, where a 1-D convolutional layer is used as the speech encoder to …
performance advantage, where a 1-D convolutional layer is used as the speech encoder to …
Deep neural mel-subband beamformer for in-car speech separation
While current deep learning (DL)-based beamforming techniques have been proved
effective in speech separation, they are often designed to process narrow-band (NB) …
effective in speech separation, they are often designed to process narrow-band (NB) …
Graph neural networks for sound source localization on distributed microphone networks
E Grinstein, M Brookes… - ICASSP 2023-2023 IEEE …, 2023 - ieeexplore.ieee.org
Distributed Microphone Arrays (DMAs) present many challenges with respect to centralized
microphone arrays. An important requirement of applications on these arrays is handling a …
microphone arrays. An important requirement of applications on these arrays is handling a …